> For the complete documentation index, see [llms.txt](https://docs.eastworld.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.eastworld.ai/whitepaper.md). # Whitepaper ### **Summary** As embodied AI moves from the lab to the real world, it needs a safe and efficient arena for evolution. The mission of Eastworld AI is to create an essential transitional stage for the arrival of general embodied intelligence: a low-cost, high-safety, and massively scalable virtual environment for key data collection, interaction validation, and capability growth. Our ultimate goal is a real world where humans and AI coexist in harmony. To achieve this, Eastworld AI adopts a forward-looking dual-track architecture that brings together two tightly coupled, co-evolving systems: * **Decentralized AI Training Subnet.** Built on Bittensor's decentralized network and incentive mechanisms, we operate a persistent open simulation world with standardized task suites and evaluation protocols. This network invites leading developers and researchers worldwide to train and validate agents together. It enables us to establish robust technical methodologies and build a durable ecosystem advantage. * **AI-Driven Immersive Game Experience.** We embed frontier AI into a game that blends tower defense and idle simulation management. The result is a new entertainment setting where humans and AI live and collaborate. It serves as an ideal testbed for long-horizon collaboration and personalized interaction, while also providing the vehicle for high-quality interaction data and a sustainable commercial loop. These two systems share core simulation technologies, continuously reinforce each other, and form a complete pathway from technical validation to real-world deployment. We are not merely creating a game. We are incubating an intelligent virtual world that points the way to the future. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.eastworld.ai/whitepaper.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.